scholarly journals Combining MLC and SVM Classifiers for Learning Based Decision Making: Analysis and Evaluations

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Yi Zhang ◽  
Jinchang Ren ◽  
Jianmin Jiang
Keyword(s):  
Breast Cancer ◽  
Decision Making ◽  
Dna Sequences ◽  
Support Vector ◽  
Nonparametric Method ◽  
Soft Decision ◽  
Vector Machines ◽  
Maximum Likelihood Classifier ◽  
Non Gaussian ◽  
Combined Classifier

Maximum likelihood classifier (MLC) and support vector machines (SVM) are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process. In this paper, MLC and SVM are combined in learning and classification, which helps to yield probabilistic output for SVM and facilitate soft decision making. In total four groups of data are used for evaluations, covering sonar, vehicle, breast cancer, and DNA sequences. The data samples are characterized in terms of Gaussian/non-Gaussian distributed and balanced/unbalanced samples which are then further used for performance assessment in comparing the SVM and the combined SVM-MLC classifier. Interesting results are reported to indicate how the combined classifier may work under various conditions.

scholarly journals Segmentation of HER2 protein overexpression in immunohistochemically stained breast cancer images using Support Vector Machines

2016 ◽  
Vol 762 ◽  
pp. 012050 ◽  
Author(s):  
Raquel Pezoa ◽  
Luis Salinas ◽  
Claudio Torres ◽  
Steffen Härtel ◽  
Cristián Maureira-Fredes ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Support Vector Machines ◽  
Support Vector ◽  
Protein Overexpression ◽  
Her2 Protein ◽  
Vector Machines

Modeling Knowledge Employee’s Turnover Based on P-SVM

2010 ◽  
Vol 121-122 ◽  
pp. 825-831
Author(s):  
Yong Zhao ◽  
Ye Zheng Liu
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Model Development ◽  
Forecast Model ◽  
Support Vector ◽  
Multi Criteria Decision Making ◽  
Parameters Selection ◽  
Vector Machines ◽  
Decision Making Problem ◽  
Simulation Results

Knowledge employee’s turnover forecast is a multi-criteria decision-making problem involving various factors. In order to forecast accurately turnover of knowledge employees, the potential support vector machines(P-SVM) is introduced to develop a turnover forecast model. In the model development, a chaos algorithm and a genetic algorithm (GA) are employed to optimize P-SVM parameters selection. The simulation results show that the model based on potential support vector machine with chaos not only has much stronger generalization ability but also has the ability of feature selection.

scholarly journals Machine Learning and Infrared Thermography for Breast Cancer Detection

Proceedings ◽  
2019 ◽  
Vol 27 (1) ◽  
pp. 45 ◽  
Author(s):  
Caroline Gonçalves ◽  
Amanda Leles ◽  
Lucimara Oliveira ◽  
Gilmar Guimaraes ◽  
Juliano Cunha ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Infrared Thermography ◽  
Cancer Detection ◽  
Low Cost ◽  
Breast Cancer Detection ◽  
Support Vector ◽  
Patients With Cancer ◽  
Vector Machines ◽  
Malignant Changes ◽  
Harmful Radiation

Breast cancer kills a large number of women around the world. Infrared thermography is a promising screening technique which does not involve harmful radiation for the patient and has a relatively low cost. This work proposes an approach for classifying patients into three different classes using infrared images: healthy patients, patients with benign changes and patients with cancer (malignant changes). A set of features is extracted from each image and two approaches are used in the classification process. The first is based on Artificial Neural Networks while the second is based on Support Vector Machines. The proposed approach shows a great potential to be used as a screening diagnosis technique for early breast cancer detection.

Directed Policy Search for Decision Making Using Relevance Vector Machines

2014 ◽  
Vol 23 (04) ◽  
pp. 1460016
Author(s):  
Ioannis Rexakis ◽  
Michail G. Lagoudakis
Keyword(s):  
Decision Making ◽  
State Space ◽  
Previous Method ◽  
The State ◽  
Computational Time ◽  
Support Vector ◽  
Learning Approaches ◽  
Policy Search ◽  
Relevance Vector Machines ◽  
Vector Machines

Several recent learning approaches in decision making under uncertainty suggest the use of classifiers for representing policies compactly. The space of possible policies, even under such structured representations, is huge and must be searched carefully to avoid computationally expensive policy simulations (rollouts). In our recent work, we proposed a method for directed exploration of policy space using support vector classifiers, whereby rollouts are directed to states around the boundaries between different action choices indicated by the separating hyperplanes in the represented policies. While effective, this method suffers from the growing number of support vectors in the underlying classifiers as the number of training examples increases. In this paper, we propose an alternative method for directed policy search based on relevance vector machines. Relevance vector machines are used both for classification (to represent a policy) and regression (to approximate the corresponding relative action advantage function). Classification is enhanced by anomaly detection for accurate policy representation. Exploiting the internal structure of the regressor, we guide the probing of the state space only to critical areas corresponding to changes of action dominance in the underlying policy. This directed focus on critical parts of the state space iteratively leads to refinement and improvement of the underlying policy and delivers excellent control policies in only a few iterations, while the small number of relevance vectors yields significant computational time savings. We demonstrate the proposed approach and compare it with our previous method on standard reinforcement learning domains (inverted pendulum and mountain car).

Splicing-site recognition of rice (Oryza sativa L.) DNA sequences by support vector machines

2003 ◽  
Vol 4 (5) ◽  
pp. 573-577
Author(s):  
Peng Si-hua ◽  
Fan Long-jiang ◽  
Peng Xiao-ning ◽  
Zhuang Shu-lin ◽  
Du Wei ◽  
...  
Keyword(s):  
Oryza Sativa ◽  
Support Vector Machines ◽  
Dna Sequences ◽  
Oryza Sativa L ◽  
Support Vector ◽  
Vector Machines ◽  
Site Recognition ◽  
Splicing Site
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